代码搜索:Approximation

找到约 1,542 项符合「Approximation」的源代码

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m nnd11fa.m

function nnd11fa(cmd,arg1) %NND11FA Function approximation demonstration. % % This demonstration requires the Neural Network Toolbox. % First Version, 8-31-95. %==============================
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m stompfigure2.m

% Figure 2: Evolution of StOMP approximation with CFAR thresholding. % Panels (a),(d),(g): successive matched filtering outputs % $\tilde{x}_1$,$\tilde{x}_2$, $\tilde{x}_3$; % Panels (b),(e),(h): s
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m ex_5_33.m

% Exercise 5.33: Parametrized l1-norm approximation % Boyd & Vandenberghe "Convex Optimization" % Jo雔le Skaf - 08/29/05 % (a figure is generated) % % Let p_star(epsilon) be the optimal value of the fo
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m norm_approx.m

% Examples 5.6,5.8: An l_p norm approximation problem % Boyd & Vandenberghe "Convex Optimization" % Jo雔le Skaf - 08/23/05 % % The goal is to show the following problem formulations give all the same %
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m clfftf.m

function [FT,FTmag,FTang] = clfftf(ft,N,Ts) % CALL: [FT,FTmag,FTang] = clfftf(ft,N,Ts) Compute the DFT % approximation of the Fourier Transform % Inputs: % ft Sampled function of time f(nTs) % N
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java cdf_normal.java

package statistics; //Gaussian CDF Taylor approximation //Code borrowed from http://www1.fpl.fs.fed.us/distributions.html 19/9 2006 /** * *This class contains routines to calculate the *normal cumul
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java cdf_normal.java

package statistics; //Gaussian CDF Taylor approximation //Code borrowed from http://www1.fpl.fs.fed.us/distributions.html 19/9 2006 /** * *This class contains routines to calculate the *normal cumul
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m que_func.m

function fofx = que_func(x) % This function computes the value of the Q-function % listed in Eq.(2.16). It uses the approximation in Eq.s (2.17) and (2.18) if (x >= 0) denom = 0.661 * x + 0.339
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java cdf_normal.java

package statistics; //Gaussian CDF Taylor approximation //Code borrowed from http://www1.fpl.fs.fed.us/distributions.html 19/9 2006 /** * *This class contains routines to calculate the *normal cumul
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hlp j_mfxscale.hlp

{smcl} {* 03mar2005}{...} {title:Rescaling warning message} {pstd} A marginal effect is a derivative. To find a derivative numerically, {cmd:mfx} uses the approximation (f(x+h)-f(x))/h. {